Radiopaedia Blog

The open source movement is revolutionising medicine. Never before in human history has there been such knowledge and opportunity available to anyone with perseverance and a connected device. In fact with enough patience, there are multiple, perhaps seemingly infinite tools and skills one can acquire, that enable quite sophisticated analysis of medical images (among many other areas of science and medicine). I’d like to explain how the ‘stars have aligned’ for this revolution and glimpse future possibilities, whilst also acknowledging a degree of hype surrounding AI and its application to medicine.

To put the present in some sort of context, my father-in-law took a computer subject at university in the seventies. In large groups, one of their assignments was to punch holes into a long piece of paper which they fed into a computer to produce a very basic game of ping pong. This computer was state-of-the-art at the time and took up several stories of the university. Mobile phone users are expected to tick over 5 billion next year, each of these capable of providing vast amounts of knowledge and at least theoretical training for many different skills to anyone who can afford one (not everyone). Who knows what computational power and device size will be common-place in another 3 or 4 decades. Futurist Ray Kurzweil has, for example, predicted that by 2049, one computer will have more computational power than the entire human race combined.

Radiopaedia was preceded by more general open source platforms. All manner of these now inhabit many corners of the web, including growing and increasingly comprehensive biobanks rich with patient-level data. The gradual specialisation of open source sites is not unique to science and medicine. For example, there are now numerous open source communities that foster the learning and progress of programming languages like python. Whilst vanilla linear and logistic regression have been around since the 1950s, now with a few lines of code we have devices that can crunch these algorithms en masse. Enter machine learning. For free at edx.org, you can spend a few hours (ok probably days or weeks) and process millions of data points to draw insights and make reasonable predictions about new data. If you are pressed for time or perhaps less technically inclined, there are palatable discussions of cutting-edge technologies: dataskeptic.com features regular podcasts from a data scientist explaining concepts to his non-data scientist partner. This one is a great start for anyone curious about applying machine learning to medical imaging. The scope and complexity of mathematical models for predictive and other analytics continues to expand and with open source code, you don’t have to be Good Will Hunting to enact them. Using radiomics techniques to predict mortality from chest CTs has been conceptually proven (Oakden-Rayner et al 2017). Machine learning (including deep learning) ought to expand the detection of pre-clinical disease states prior to the patient developing symptoms and could be a stimulus for much wider uptake of medical imaging. Such a proliferation of image acquisition poses another set of questions to the radiology field. Pre-clinical detection is applicable to some diseases more than others but perhaps even apparently unforeseeable conditions like major trauma will one day be accurately predicted by a network of biobanks, machine learning algorithms and an internet of things (IoT). Regardless, technological advances spur on precision medicine which will eventually be genome and probably environment specific. Social and ethical debates about how this may widen the gap between the ‘haves’ and ‘have-nots’ are inevitable and desirable. There are many examples of how technological advances disperse for global benefit; mobile phones and Radiopaedia itself are great examples of these.

At least for the next 30 years, there will always be radiologists in some form or another. We are a long way from any form of AI being able to listen to, digest and give salient advice about complex medical histories and examinations; point out pivotal features in selecting different modalities to colleagues; be perspicacious in high-stakes multi-disciplinary meetings or perform complicated procedures. There are also less easily defined roles for the human touch, the laying-on-of-hands or the thoughtful, attentive and knowing nod that patients appreciate and as any clinician will identify, can be seemingly therapeutic in and of themselves. For the foreseeable future, deep learning algorithms rely on more than just a handful of examples for a given condition. The deep learning results prompted by the UK’s NIH open source chest x-ray database, whilst pivotal and theoretically exciting, have been confined to certain entities (eg pneumonia, cardiomegaly, pneumothorax etc) and not currently feasible for workstation, coal-face translation. It will be a while yet before workstation software can effectively point out uncommon findings like Luftsichel sign. So for at least a few decades to come, Radiopaedia will be a valid tool for us humans recognising rare and uncommon conditions and trainees will still be pouring over thousands of chest x-rays each.

This combination of open source capabilities is the very exciting infancy of radiomics - beyond what is (my new favourite term...) ‘human-readable’. We can now process medical image data on a scale that would make Wilhelm Roentgen physically (and metaphysically) ill. It is an incredibly exciting time to be a part of what some are calling ‘the fourth industrial revolution’. Only time will tell if these kind of statements are hype but for sure, we have only just now witnessed the tip of the open source, medical data iceberg and Radiopaedia is strapped in for the ride.

About the author: James Condon graduated from medicine 2014 and is commencing as a PhD candidate 2018 in the use of computer vision for medical image interpretation. He works casually in emergency medicine and clinical trials and has previously completed a range of medical and surgical rotations in Adelaide.

Disclosure: J. Condon is commencing independent post-graduate research with G. Carneiro and L. Palmer, co-authors of a journal article referenced in this piece. They were not involved in the writing of this blog.

Disclaimer: Views expressed in blog posts are those of the author and not necessarily those of Radiopaedia.org or his/her employer.

I'm sorry to say that on Friday 13th we have been the subject of a bot attack that has caused significant technical headaches. The form of this attack was the automated creation of tens of thousands of new accounts all using spam email addresses.

There is no indication whatsoever that there has been any attempt at breaching our security and there is no indication whatsoever that any user details have been obtained. This seems to have been purely an automated attempt to create a large number of user profiles and it is likely that we were not even a deliberate target.

We had to briefly suspend new account creations and introduce a rushed urgent temporary fix. Although a number of bugs resulting directly from this initial fix and indirectly from the tens of thousands of emails we sent during the account creation process we believe all have now been corrected.

Please let us know if you encounter anything new by writing to us at general@radiopaedia.org.

Over the years, the number of things you can do on Radiopaedia has increased. You can collect cases, contribute to articles, collate playlists, mark cases and articles as favourites, watch or attend our courses. You need to also have access to a bunch of account settings. These used to be scattered all over the place. Well no more.

In addition to this, we have also taken this opportunity to launch achievements. These are a way of recognising the contribution a user, including you, has made to the site. These badges are shown on your public profile. You level-up as you contribute to the site in a variety of ways.

And lastly, and this is especially important to all you who have hundreds of cases, playlists and favourites, you can now search and filter just your own collection.

I hope you enjoy using these new features as much as we have enjoyed creating them for you.

Frank

Associate Professor Frank Gaillard is the Founder and Editor in Chief of Radiopaedia.org. He is also an academic neuroradiologist and Director of Research in the Radiology Department of the Royal Melbourne Hospital/University of Melbourne in Melbourne, Australia.

Almost 10 years ago I coded up the first Radiopaedia iOS app. Since then we have had a few different version and a small but useful amount of content. One of the reasons for its existence was that at the time the website was horrible to use on mobile devices. Since then, we have spent a significant amount of effort in improving the mobile version of the app and have created new ways for users to curate and share cases.

It is, therefore, time to say goodbye to the Radiopaedia iOS app.

For now, the app still works and we will be leaving it on the app store and all packs are free. Within a month or so (March 2018 or so) we will be removing it from the app store. At that time the app should continue to work on your devices but you won't be able to download it again or access new content.

Update: The app has been removed from the App Store (late March 2018)

I wanted to thank all of you for your patronage. The small proceeds from the sale of the case packs have helped pay for the continued development of the site.

Frank

Associate Professor Frank Gaillard is the Founder and Editor in Chief of Radiopaedia.org. He is also an academic neuroradiologist and Director of Research in the Radiology Department of the Royal Melbourne Hospital/University of Melbourne in Melbourne, Australia.

Radiopaedia.org and the American Society of Neuroradiology (ASNR) are again collaborating on giving you all the opportunity to submit an adult brain case to ASNR 2018 Case of the Day.

Each day during the ASNR 56th Annual Meeting (June 2 - 7) in Vancouver, BC, Canada a case will be shown as the official Case of the Day. This has traditionally been 'invite only', but just like last year, this year one of the cases will be chosen from cases you submit to Radiopaedia.org.

In addition to one ASNR 2018 case of the day winner, we will also be showcasing a number of the best submissions as our very own Radiopaedia.org 'cases of the day' on our home page and through social media. And, even better, you will be contributing to your personal case library and making Radiopaedia.org even better!

Prizes

There are a number of prizes available:

Winner

The winner gets two awesome prizes:

Standard Room for two (2) nights at the meeting venue at Fairmont Waterfront Hotel and including complimentary daily in-room WiFi and health club access (value of USD$545).

The prize is courtesy of the American Society of Neuroradiology (ASNR). The reservation can be used at any point during the ASNR 56th Annual Meeting dates from Friday, June 1 through Thursday, June 7. If you are not planning to attend the conference, then that's ok. You will receive the prize either way, and you can, if you wish, transfer it.

Submitting a case is easy, especially if you are using one of our case uploaders. If not, then you can do it the old-fashioned browser-based way. If you are not already familiar with how this works, this short video will help.

Dates

Submissions close on February 28th 2018, and the winner will be chosen by ASNR committee in the following couple of weeks. The winner will then be contacted by email, so please make sure the email listed in your Radiopaedia.org profile is correct.

Poster

The winner will then be asked to take a few choice images from their case and make a two-slide powerpoint poster (Question/Answer) which will be shown at the actual conference. This is not an onerous task, and the template will be provided to you. Here is an example.

A physical poster will also be printed from your slides (by ASNR) and shown. This will be done for you, so if you are not attending, it is not a problem.